In this section, we evaluate the empirical performance of the proposed algorithms against existing algorithms for the tensor completion problem in the TT format. Other tested algorithms, including Riemannian gradient descent (RGD (E)) [1]}, [2]}, Riemannian conjugate gradient (RCG (E)) [3]}, Riemannian trust region with finite-difference Hessian approximation (FD-TR) [4]}, are all based on the embedded geometry and implemented in the toolbox Manopt [5]}. For a fair comparison, the step size selection criterion of RGD (E) has been modified to backtracking line search with RBB initial step size. We first compare the recovery ability of the tested algorithms on random low-rank tensors in Section REF . Then the convergence performance of these algorithms are tested in Section REF . Finally, we evaluate the reconstruction quality of the tested algorithms on function-related tensors in Section REF .
